AI Engineer

Job Description

Role Overview

We are seeking a Senior AI Solution Architect to lead the design and delivery of enterprise-scale AI/ML and Generative AI solutions for manufacturing and industrial clients. The role requires expertise in LLM architectures, RAG pipelines, agentic AI systems, predictive maintenance, and digital twins, combined with strong stakeholder engagement and enterprise integration skills.

Key Responsibilities:

• AI Solution Design: Architect scalable AI/ML platforms, GenAI ecosystems, and agentic AI workflows tailored for manufacturing and Industry 4.0.
• Use Case Feasibility: Assess and validate AI/ML use cases across plant operations, supply chain, predictive maintenance, and digital factory initiatives.
• AI/ML Architecture: Design RAG pipelines, vector database integrations, LLM orchestration, and multi-agent systems using LangChain, LangGraph, MCP, and Azure/AWS/GCP AI services.
• Enterprise Integration: Integrate AI solutions with ERP (SAP S/4HANA, Oracle), MES, SCADA/DCS, IoT platforms, and enterprise data lakes.
• Data Requirement Definition: Define data pipelines, governance frameworks, and observability models for industrial and manufacturing datasets.
• Model Performance & Validation: Establish MLOps, drift monitoring, governance, and responsible AI frameworks ensuring production-grade reliability.
• Human-in-the-Loop Design: Incorporate HITL workflows for safety-critical manufacturing AI systems.
• Implementation Estimation: Provide effort estimation, delivery roadmaps, and cost optimization strategies for AI programs.
• Executive Communication: Engage with CXOs, plant leaders, and global stakeholders to align AI investments with business outcomes.

Required Skills

• Strong expertise in Generative AI, Agentic AI, LLMs, RAG pipelines, embeddings, and vector databases.
• Hands-on with Azure OpenAI, AWS Bedrock, GCP Vertex AI, IBM WatsonX, OpenShift AI.
• Experience with LangChain, LangGraph, MCP, CrewAI, Pinecone, ChromaDB, Qdrant, FAISS.
• Knowledge of Industry 4.0, predictive maintenance, digital twins, IoT/OT convergence.
• Cloud-native architecture (AWS, Azure, GCP), microservices, Kubernetes, Docker, CI/CD.
• Strong stakeholder management, pre-sales solutioning, and executive advisory skills.

Preferred Background

• Proven delivery in manufacturing, automotive, aerospace, steel, semiconductors, or supply chain domains.
• Experience in enterprise-scale AI programs with measurable business impact.
• Ph.D./Master's in Computer Science, AI/ML, or related fields preferred.
• Patents, publications, or thought leadership in AI/ML/GenAI are a plus.